{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://mp.weixin.qq.com/s?__biz=MzAxNjc1MDUyOQ==&mid=2247501902&idx=1&sn=4b953c517744ad3163e96cd5c3c29dac&chksm=9bf28ae0ac8503f6ebc07ad5eabd9c0d2ecd0266114834daf663ceccbc6c38fb0bd1184be4f3&scene=178&cur_album_id=3313836549188583425#rd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading bar data...\n",
      "Loaded bar data: 0:00:00 \n",
      "\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 1/20, Training Loss: 0.03730928695295006\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 2/20, Training Loss: 0.004322780249640346\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 3/20, Training Loss: 0.002967718717021247\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 4/20, Training Loss: 0.0023402247888346514\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 5/20, Training Loss: 0.001902272905378292\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 6/20, Training Loss: 0.0015750153766324122\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 7/20, Training Loss: 0.0016188478524175782\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 8/20, Training Loss: 0.0014746031723916531\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 9/20, Training Loss: 0.0012407041698073347\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 10/20, Training Loss: 0.0014143136446364223\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 11/20, Training Loss: 0.0011185079764497155\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 12/20, Training Loss: 0.0010340748548818132\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 13/20, Training Loss: 0.001119004797268038\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 14/20, Training Loss: 0.001102830002976892\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 15/20, Training Loss: 0.0010486915688185641\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 16/20, Training Loss: 0.0008952313393820078\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 17/20, Training Loss: 0.0008859413268510252\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 18/20, Training Loss: 0.0008526889607310296\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 19/20, Training Loss: 0.0008241287587831418\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([19, 64])\n",
      "Query shape: torch.Size([19, 1, 64]), Key shape: torch.Size([19, 1, 64]), Value shape: torch.Size([19, 1, 64])\n",
      "Transformed Q shape: torch.Size([19, 1, 64]), K shape: torch.Size([19, 1, 64]), V shape: torch.Size([19, 1, 64])\n",
      "Attention weights shape: torch.Size([19, 1, 1])\n",
      "Attention output shape: torch.Size([19, 1, 64])\n",
      "Model output shape: torch.Size([19, 1])\n",
      "Epoch 20/20, Training Loss: 0.0008411257789703086\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([32, 64])\n",
      "Query shape: torch.Size([32, 1, 64]), Key shape: torch.Size([32, 1, 64]), Value shape: torch.Size([32, 1, 64])\n",
      "Transformed Q shape: torch.Size([32, 1, 64]), K shape: torch.Size([32, 1, 64]), V shape: torch.Size([32, 1, 64])\n",
      "Attention weights shape: torch.Size([32, 1, 1])\n",
      "Attention output shape: torch.Size([32, 1, 64])\n",
      "Model output shape: torch.Size([32, 1])\n",
      "Input to kernel attention shape: torch.Size([13, 64])\n",
      "Query shape: torch.Size([13, 1, 64]), Key shape: torch.Size([13, 1, 64]), Value shape: torch.Size([13, 1, 64])\n",
      "Transformed Q shape: torch.Size([13, 1, 64]), K shape: torch.Size([13, 1, 64]), V shape: torch.Size([13, 1, 64])\n",
      "Attention weights shape: torch.Size([13, 1, 1])\n",
      "Attention output shape: torch.Size([13, 1, 64])\n",
      "Model output shape: torch.Size([13, 1])\n",
      "Test Loss: 0.0003221978331566788\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pybroker.ext.data import AKShare\n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "ak = AKShare()\n",
    "\n",
    "# Load Reliance historical data\n",
    "data = ak.query(symbols=\"000001\", start_date=\"2019-01-01\", end_date=\"2024-01-01\", adjust=\"qfq\")\n",
    "\n",
    "# Preprocess data\n",
    "data = data[['close']].dropna()  # Keep only the 'Close' price and drop any missing values\n",
    "\n",
    "# Normalize the data\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "data['close'] = scaler.fit_transform(data['close'].values.reshape(-1, 1))\n",
    "\n",
    "# Create sliding window sequences\n",
    "def create_sequences(data, window_size):\n",
    "    sequences = []\n",
    "    labels = []\n",
    "    for i in range(len(data) - window_size):\n",
    "        sequences.append(data[i:i + window_size])\n",
    "        labels.append(data[i + window_size])\n",
    "    return np.array(sequences), np.array(labels)\n",
    "\n",
    "window_size = 30  # Sliding window size of 30 days\n",
    "X, y = create_sequences(data['close'].values, window_size)\n",
    "train_size = int(0.8 * len(X))  # 80% for training\n",
    "X_train, X_test = X[:train_size], X[train_size:]\n",
    "y_train, y_test = y[:train_size], y[train_size:]\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "X_train_tensor = torch.tensor(X_train, dtype=torch.float32).unsqueeze(-1)  # Shape: (batch_size, seq_len, 1)\n",
    "y_train_tensor = torch.tensor(y_train, dtype=torch.float32).unsqueeze(-1)  # Shape: (batch_size, 1)\n",
    "X_test_tensor = torch.tensor(X_test, dtype=torch.float32).unsqueeze(-1)    # Shape: (batch_size, seq_len, 1)\n",
    "y_test_tensor = torch.tensor(y_test, dtype=torch.float32).unsqueeze(-1)    # Shape: (batch_size, 1)\n",
    "\n",
    "# Create PyTorch Dataset and DataLoader\n",
    "class TimeSeriesDataset(Dataset):\n",
    "    def __init__(self, X, y):\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.X)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.X[idx], self.y[idx]\n",
    "\n",
    "train_dataset = TimeSeriesDataset(X_train_tensor, y_train_tensor)\n",
    "test_dataset = TimeSeriesDataset(X_test_tensor, y_test_tensor)\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
    "\n",
    "# Define a custom kernel attention layer\n",
    "class KernelAttention(nn.Module):\n",
    "    def __init__(self, d_model):\n",
    "        super(KernelAttention, self).__init__()\n",
    "        self.d_model = d_model\n",
    "        self.query_layer = nn.Linear(d_model, d_model)\n",
    "        self.key_layer = nn.Linear(d_model, d_model)\n",
    "        self.value_layer = nn.Linear(d_model, d_model)\n",
    "\n",
    "    def forward(self, query, key, value):\n",
    "        # Ensure correct tensor shapes for debugging\n",
    "        print(f\"Query shape: {query.shape}, Key shape: {key.shape}, Value shape: {value.shape}\")\n",
    "\n",
    "        # Apply linear transformations\n",
    "        Q = self.query_layer(query)\n",
    "        K = self.key_layer(key)\n",
    "        V = self.value_layer(value)\n",
    "\n",
    "        print(f\"Transformed Q shape: {Q.shape}, K shape: {K.shape}, V shape: {V.shape}\")\n",
    "\n",
    "        # Compute pairwise distances and apply Gaussian kernel\n",
    "        pairwise_distances = torch.cdist(Q, K, p=2)  # Compute pairwise Euclidean distance\n",
    "        kernel_weights = torch.exp(-pairwise_distances ** 2 / (2 * 0.5 ** 2))  # Gaussian kernel\n",
    "\n",
    "        # Apply softmax to obtain attention weights\n",
    "        attention_weights = F.softmax(kernel_weights, dim=-1)\n",
    "        print(f\"Attention weights shape: {attention_weights.shape}\")\n",
    "\n",
    "        # Ensure V has shape (batch_size, seq_len, d_model)\n",
    "        V = V.view(V.shape[0], -1, self.d_model)  # Reshape V to (batch_size, seq_len, d_model)\n",
    "\n",
    "        # Compute attention output\n",
    "        output = torch.bmm(attention_weights, V)  # Shape: (batch_size, seq_len, d_model)\n",
    "        print(f\"Attention output shape: {output.shape}\")\n",
    "\n",
    "        return output\n",
    "\n",
    "# Define the Transformer Decoder model with Kernel Attention\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, window_size, d_model, output_dim):\n",
    "        super(TransformerDecoder, self).__init__()\n",
    "        self.kernel_attention = KernelAttention(d_model)\n",
    "        self.fc1 = nn.Linear(window_size, d_model)\n",
    "        self.fc2 = nn.Linear(d_model, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Reshape input for linear layer\n",
    "        x = x.view(x.size(0), -1)  # Flatten from (batch_size, seq_len, 1) to (batch_size, seq_len)\n",
    "        x = self.fc1(x)\n",
    "        print(f\"Input to kernel attention shape: {x.shape}\")\n",
    "\n",
    "        # Reshape x to match expected attention input shape\n",
    "        x = x.view(x.size(0), -1, self.kernel_attention.d_model)  # Shape: (batch_size, seq_len, d_model)\n",
    "        x = self.kernel_attention(x, x, x)  # Self-attention\n",
    "        x = F.relu(x)\n",
    "        x = torch.mean(x, dim=1)  # Reduce to get fixed output size\n",
    "        output = self.fc2(x)\n",
    "        print(f\"Model output shape: {output.shape}\")\n",
    "\n",
    "        return output\n",
    "\n",
    "# Instantiate the model\n",
    "d_model = 64  # Embedding dimension\n",
    "output_dim = 1\n",
    "model = TransformerDecoder(window_size, d_model, output_dim)\n",
    "\n",
    "# Define loss and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# Training loop\n",
    "epochs = 20\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "    for X_batch, y_batch in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        output = model(X_batch)\n",
    "        loss = criterion(output, y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        total_loss += loss.item()\n",
    "\n",
    "    print(f\"Epoch {epoch+1}/{epochs}, Training Loss: {total_loss / len(train_loader)}\")\n",
    "\n",
    "# Evaluate on test data\n",
    "model.eval()\n",
    "test_loss = 0\n",
    "predictions = []\n",
    "with torch.no_grad():\n",
    "    for X_batch, y_batch in test_loader:\n",
    "        output = model(X_batch)\n",
    "        loss = criterion(output, y_batch)\n",
    "        test_loss += loss.item()\n",
    "        predictions.append(output.numpy())\n",
    "\n",
    "print(f\"Test Loss: {test_loss / len(test_loader)}\")\n",
    "\n",
    "# Convert predictions back to original scale\n",
    "predictions = np.concatenate(predictions).flatten()\n",
    "y_test_actual = scaler.inverse_transform(y_test.reshape(-1, 1)).flatten()\n",
    "predictions_actual = scaler.inverse_transform(predictions.reshape(-1, 1)).flatten()\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(len(y_test_actual)), y_test_actual, label='Actual Prices')\n",
    "plt.plot(range(len(predictions_actual)), predictions_actual, label='Predicted Prices')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Actual vs Predicted Prices for Reliance')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading bar data...\n",
      "Loaded bar data: 0:00:00 \n",
      "\n",
      "Epoch 1/20 (nonlinear), Training Loss: 0.05533459526486695\n",
      "Epoch 1/20 (nonlinear), Test Loss: 0.006380181323038414\n",
      "Epoch 2/20 (nonlinear), Training Loss: 0.005845232354477048\n",
      "Epoch 2/20 (nonlinear), Test Loss: 0.004031045871670358\n",
      "Epoch 3/20 (nonlinear), Training Loss: 0.004669575400960942\n",
      "Epoch 3/20 (nonlinear), Test Loss: 0.0026361278287367895\n",
      "Epoch 4/20 (nonlinear), Training Loss: 0.0037077690629909437\n",
      "Epoch 4/20 (nonlinear), Test Loss: 0.0020438833016669378\n",
      "Epoch 5/20 (nonlinear), Training Loss: 0.002994232058214645\n",
      "Epoch 5/20 (nonlinear), Test Loss: 0.0013260816849651746\n",
      "Epoch 6/20 (nonlinear), Training Loss: 0.0021588487434200943\n",
      "Epoch 6/20 (nonlinear), Test Loss: 0.0008021357680263463\n",
      "Epoch 7/20 (nonlinear), Training Loss: 0.0018049365219970543\n",
      "Epoch 7/20 (nonlinear), Test Loss: 0.0006129586090537487\n",
      "Epoch 8/20 (nonlinear), Training Loss: 0.00160540429642424\n",
      "Epoch 8/20 (nonlinear), Test Loss: 0.0006544804691657191\n",
      "Epoch 9/20 (nonlinear), Training Loss: 0.0014272240572609008\n",
      "Epoch 9/20 (nonlinear), Test Loss: 0.0005663863075824338\n",
      "Epoch 10/20 (nonlinear), Training Loss: 0.0013993605175831665\n",
      "Epoch 10/20 (nonlinear), Test Loss: 0.0004653690057239146\n",
      "Epoch 11/20 (nonlinear), Training Loss: 0.0012608140086134275\n",
      "Epoch 11/20 (nonlinear), Test Loss: 0.00044066030659450917\n",
      "Epoch 12/20 (nonlinear), Training Loss: 0.001331158655618007\n",
      "Epoch 12/20 (nonlinear), Test Loss: 0.000395198833757604\n",
      "Epoch 13/20 (nonlinear), Training Loss: 0.0013806338916765525\n",
      "Epoch 13/20 (nonlinear), Test Loss: 0.0003919094842785853\n",
      "Epoch 14/20 (nonlinear), Training Loss: 0.0012140740640461446\n",
      "Epoch 14/20 (nonlinear), Test Loss: 0.0003970352763644769\n",
      "Epoch 15/20 (nonlinear), Training Loss: 0.0012218847482775648\n",
      "Epoch 15/20 (nonlinear), Test Loss: 0.0004039148298033979\n",
      "Epoch 16/20 (nonlinear), Training Loss: 0.0010538262412107238\n",
      "Epoch 16/20 (nonlinear), Test Loss: 0.0003477685268080677\n",
      "Epoch 17/20 (nonlinear), Training Loss: 0.001032026909524575\n",
      "Epoch 17/20 (nonlinear), Test Loss: 0.00032619557896396145\n",
      "Epoch 18/20 (nonlinear), Training Loss: 0.0010449242341564967\n",
      "Epoch 18/20 (nonlinear), Test Loss: 0.00037292459182936\n",
      "Epoch 19/20 (nonlinear), Training Loss: 0.0008921088320979228\n",
      "Epoch 19/20 (nonlinear), Test Loss: 0.00042722667967609596\n",
      "Epoch 20/20 (nonlinear), Training Loss: 0.0009424228192074224\n",
      "Epoch 20/20 (nonlinear), Test Loss: 0.0003253890936321113\n",
      "Train MAE (nonlinear): 3.6939, Train MAPE (nonlinear): 25.32%\n",
      "Test MAE (nonlinear): 0.2090, Test MAPE (nonlinear): 2.00%\n",
      "Epoch 1/20 (linear), Training Loss: 0.020819824282079936\n",
      "Epoch 1/20 (linear), Test Loss: 0.011084327299613506\n",
      "Epoch 2/20 (linear), Training Loss: 0.004625138795624177\n",
      "Epoch 2/20 (linear), Test Loss: 0.0026442724920343608\n",
      "Epoch 3/20 (linear), Training Loss: 0.002587520705613618\n",
      "Epoch 3/20 (linear), Test Loss: 0.0006284137871261919\n",
      "Epoch 4/20 (linear), Training Loss: 0.0018640225074098755\n",
      "Epoch 4/20 (linear), Test Loss: 0.00047851463250481174\n",
      "Epoch 5/20 (linear), Training Loss: 0.0018175400366696219\n",
      "Epoch 5/20 (linear), Test Loss: 0.0008805633551673964\n",
      "Epoch 6/20 (linear), Training Loss: 0.0014787498337682336\n",
      "Epoch 6/20 (linear), Test Loss: 0.00035253059559181565\n",
      "Epoch 7/20 (linear), Training Loss: 0.0013494548892291883\n",
      "Epoch 7/20 (linear), Test Loss: 0.0004050740408274578\n",
      "Epoch 8/20 (linear), Training Loss: 0.0012167423352366312\n",
      "Epoch 8/20 (linear), Test Loss: 0.00033575410134289996\n",
      "Epoch 9/20 (linear), Training Loss: 0.0011895744188223033\n",
      "Epoch 9/20 (linear), Test Loss: 0.0004772854681505123\n",
      "Epoch 10/20 (linear), Training Loss: 0.0010310502315405757\n",
      "Epoch 10/20 (linear), Test Loss: 0.0003908709786628606\n",
      "Epoch 11/20 (linear), Training Loss: 0.000908085994888097\n",
      "Epoch 11/20 (linear), Test Loss: 0.00030731444394405116\n",
      "Epoch 12/20 (linear), Training Loss: 0.0012482078231793518\n",
      "Epoch 12/20 (linear), Test Loss: 0.00037070438975206343\n",
      "Epoch 13/20 (linear), Training Loss: 0.00083766819734592\n",
      "Epoch 13/20 (linear), Test Loss: 0.0003205859911759035\n",
      "Epoch 14/20 (linear), Training Loss: 0.0008268552172618608\n",
      "Epoch 14/20 (linear), Test Loss: 0.00032824446134327445\n",
      "Epoch 15/20 (linear), Training Loss: 0.000933842074785692\n",
      "Epoch 15/20 (linear), Test Loss: 0.0002488233890289848\n",
      "Epoch 16/20 (linear), Training Loss: 0.0010374677076470107\n",
      "Epoch 16/20 (linear), Test Loss: 0.0003910325340257259\n",
      "Epoch 17/20 (linear), Training Loss: 0.001044345018453896\n",
      "Epoch 17/20 (linear), Test Loss: 0.00031105673315323656\n",
      "Epoch 18/20 (linear), Training Loss: 0.0008069095832373326\n",
      "Epoch 18/20 (linear), Test Loss: 0.0002722027338677435\n",
      "Epoch 19/20 (linear), Training Loss: 0.0007167127565480769\n",
      "Epoch 19/20 (linear), Test Loss: 0.00025568779528839514\n",
      "Epoch 20/20 (linear), Training Loss: 0.00071164924884215\n",
      "Epoch 20/20 (linear), Test Loss: 0.0003657992510852637\n",
      "Train MAE (linear): 3.7274, Train MAPE (linear): 25.53%\n",
      "Test MAE (linear): 0.2466, Test MAPE (linear): 2.36%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nonlinear Attention: Train MAE: 3.6939, Train MAPE: 25.32%\n",
      "Nonlinear Attention: Test MAE: 0.2090, Test MAPE: 2.00%\n",
      "Linear Attention: Train MAE: 3.7274, Train MAPE: 25.53%\n",
      "Linear Attention: Test MAE: 0.2466, Test MAPE: 2.36%\n"
     ]
    }
   ],
   "source": [
    "from pybroker.ext.data import AKShare\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "ak = AKShare()\n",
    "\n",
    "# Load Reliance historical data\n",
    "data = ak.query(symbols=\"000001\", start_date=\"2019-01-01\", end_date=\"2024-01-01\", adjust=\"qfq\")\n",
    "\n",
    "# Preprocess data\n",
    "data = data[['close']].dropna()  # Keep only the 'Close' price and drop any missing values\n",
    "\n",
    "# Normalize the data\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "data['close'] = scaler.fit_transform(data['close'].values.reshape(-1, 1))\n",
    "\n",
    "# Create sliding window sequences\n",
    "def create_sequences(data, window_size):\n",
    "    sequences = []\n",
    "    labels = []\n",
    "    for i in range(len(data) - window_size):\n",
    "        sequences.append(data[i:i + window_size])\n",
    "        labels.append(data[i + window_size])\n",
    "    return np.array(sequences), np.array(labels)\n",
    "\n",
    "window_size = 30  # Sliding window size of 30 days\n",
    "X, y = create_sequences(data['close'].values, window_size)\n",
    "train_size = int(0.8 * len(X))  # 80% for training\n",
    "X_train, X_test = X[:train_size], X[train_size:]\n",
    "y_train, y_test = y[:train_size], y[train_size:]\n",
    "\n",
    "# Convert data to PyTorch tensors\n",
    "X_train_tensor = torch.tensor(X_train, dtype=torch.float32).unsqueeze(-1)  # Shape: (batch_size, seq_len, 1)\n",
    "y_train_tensor = torch.tensor(y_train, dtype=torch.float32).unsqueeze(-1)  # Shape: (batch_size, 1)\n",
    "X_test_tensor = torch.tensor(X_test, dtype=torch.float32).unsqueeze(-1)    # Shape: (batch_size, seq_len, 1)\n",
    "y_test_tensor = torch.tensor(y_test, dtype=torch.float32).unsqueeze(-1)    # Shape: (batch_size, 1)\n",
    "\n",
    "# Create PyTorch Dataset and DataLoader\n",
    "class TimeSeriesDataset(Dataset):\n",
    "    def __init__(self, X, y):\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.X)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.X[idx], self.y[idx]\n",
    "\n",
    "train_dataset = TimeSeriesDataset(X_train_tensor, y_train_tensor)\n",
    "test_dataset = TimeSeriesDataset(X_test_tensor, y_test_tensor)\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
    "\n",
    "# Define a custom kernel attention layer\n",
    "class KernelAttention(nn.Module):\n",
    "    def __init__(self, d_model):\n",
    "        super(KernelAttention, self).__init__()\n",
    "        self.d_model = d_model\n",
    "        self.query_layer = nn.Linear(d_model, d_model)\n",
    "        self.key_layer = nn.Linear(d_model, d_model)\n",
    "        self.value_layer = nn.Linear(d_model, d_model)\n",
    "\n",
    "    def forward(self, query, key, value):\n",
    "        # Apply linear transformations\n",
    "        Q = self.query_layer(query)\n",
    "        K = self.key_layer(key)\n",
    "        V = self.value_layer(value)\n",
    "\n",
    "        # Compute pairwise distances and apply Gaussian kernel\n",
    "        pairwise_distances = torch.cdist(Q, K, p=2)  # Compute pairwise Euclidean distance\n",
    "        kernel_weights = torch.exp(-pairwise_distances ** 2 / (2 * 0.5 ** 2))  # Gaussian kernel\n",
    "\n",
    "        # Apply softmax to obtain attention weights\n",
    "        attention_weights = F.softmax(kernel_weights, dim=-1)\n",
    "\n",
    "        # Ensure V has shape (batch_size, seq_len, d_model)\n",
    "        V = V.view(V.shape[0], -1, self.d_model)  # Reshape V to (batch_size, seq_len, d_model)\n",
    "\n",
    "        # Compute attention output\n",
    "        output = torch.bmm(attention_weights, V)  # Shape: (batch_size, seq_len, d_model)\n",
    "\n",
    "        return output\n",
    "\n",
    "# Define the Linear Attention layer for comparison\n",
    "class LinearAttention(nn.Module):\n",
    "    def __init__(self, d_model):\n",
    "        super(LinearAttention, self).__init__()\n",
    "        self.d_model = d_model\n",
    "        self.attention = nn.MultiheadAttention(embed_dim=d_model, num_heads=1)\n",
    "\n",
    "    def forward(self, query, key, value):\n",
    "        query = query.permute(1, 0, 2)  # Rearrange for attention layer (seq_len, batch_size, d_model)\n",
    "        key = key.permute(1, 0, 2)\n",
    "        value = value.permute(1, 0, 2)\n",
    "\n",
    "        # Perform standard multi-head self-attention\n",
    "        output, _ = self.attention(query, key, value)\n",
    "        output = output.permute(1, 0, 2)  # Rearrange back to (batch_size, seq_len, d_model)\n",
    "        return output\n",
    "\n",
    "# Define the Transformer Decoder model with both attentions\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, window_size, d_model, output_dim, attention_type='nonlinear'):\n",
    "        super(TransformerDecoder, self).__init__()\n",
    "        if attention_type == 'nonlinear':\n",
    "            self.attention_layer = KernelAttention(d_model)\n",
    "        elif attention_type == 'linear':\n",
    "            self.attention_layer = LinearAttention(d_model)\n",
    "        else:\n",
    "            raise ValueError(\"Unsupported attention type. Choose either 'linear' or 'nonlinear'.\")\n",
    "\n",
    "        self.fc1 = nn.Linear(window_size, d_model)\n",
    "        self.fc2 = nn.Linear(d_model, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Reshape input for linear layer\n",
    "        x = x.view(x.size(0), -1)  # Flatten from (batch_size, seq_len, 1) to (batch_size, seq_len)\n",
    "        x = self.fc1(x)\n",
    "\n",
    "        # Reshape x to match expected attention input shape\n",
    "        x = x.view(x.size(0), -1, self.attention_layer.d_model)  # Shape: (batch_size, seq_len, d_model)\n",
    "        x = self.attention_layer(x, x, x)  # Attention mechanism\n",
    "        x = F.relu(x)\n",
    "        x = torch.mean(x, dim=1)  # Reduce to get fixed output size\n",
    "        output = self.fc2(x)\n",
    "\n",
    "        return output\n",
    "\n",
    "# Train and evaluate both models\n",
    "def train_model(attention_type):\n",
    "    # Instantiate the model\n",
    "    d_model = 64  # Embedding dimension\n",
    "    output_dim = 1\n",
    "    model = TransformerDecoder(window_size, d_model, output_dim, attention_type=attention_type)\n",
    "\n",
    "    # Define loss and optimizer\n",
    "    criterion = nn.MSELoss()\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "    # Training loop\n",
    "    epochs = 20\n",
    "    train_losses = []\n",
    "    test_losses = []\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        total_loss = 0\n",
    "        for X_batch, y_batch in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            output = model(X_batch)\n",
    "            loss = criterion(output, y_batch)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            total_loss += loss.item()\n",
    "        train_losses.append(total_loss / len(train_loader))\n",
    "        print(f\"Epoch {epoch+1}/{epochs} ({attention_type}), Training Loss: {train_losses[-1]}\")\n",
    "\n",
    "        # Evaluate on test data\n",
    "        model.eval()\n",
    "        test_loss = 0\n",
    "        with torch.no_grad():\n",
    "            for X_batch, y_batch in test_loader:\n",
    "                output = model(X_batch)\n",
    "                loss = criterion(output, y_batch)\n",
    "                test_loss += loss.item()\n",
    "        test_losses.append(test_loss / len(test_loader))\n",
    "        print(f\"Epoch {epoch+1}/{epochs} ({attention_type}), Test Loss: {test_losses[-1]}\")\n",
    "\n",
    "    # Get predictions for train and test data\n",
    "    model.eval()\n",
    "    train_predictions = []\n",
    "    test_predictions = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for X_batch, _ in train_loader:\n",
    "            train_output = model(X_batch)\n",
    "            train_predictions.append(train_output.numpy())\n",
    "\n",
    "        for X_batch, _ in test_loader:\n",
    "            test_output = model(X_batch)\n",
    "            test_predictions.append(test_output.numpy())\n",
    "\n",
    "    train_predictions = np.concatenate(train_predictions).flatten()\n",
    "    test_predictions = np.concatenate(test_predictions).flatten()\n",
    "\n",
    "    # Convert predictions back to original scale\n",
    "    y_train_actual = scaler.inverse_transform(y_train.reshape(-1, 1)).flatten()\n",
    "    y_test_actual = scaler.inverse_transform(y_test.reshape(-1, 1)).flatten()\n",
    "    train_predictions_actual = scaler.inverse_transform(train_predictions.reshape(-1, 1)).flatten()\n",
    "    test_predictions_actual = scaler.inverse_transform(test_predictions.reshape(-1, 1)).flatten()\n",
    "\n",
    "    # Calculate MAE and MAPE\n",
    "    mae_train = np.mean(np.abs(y_train_actual - train_predictions_actual))\n",
    "    mae_test = np.mean(np.abs(y_test_actual - test_predictions_actual))\n",
    "    mape_train = np.mean(np.abs((y_train_actual - train_predictions_actual) / y_train_actual)) * 100\n",
    "    mape_test = np.mean(np.abs((y_test_actual - test_predictions_actual) / y_test_actual)) * 100\n",
    "\n",
    "    print(f\"Train MAE ({attention_type}): {mae_train:.4f}, Train MAPE ({attention_type}): {mape_train:.2f}%\")\n",
    "    print(f\"Test MAE ({attention_type}): {mae_test:.4f}, Test MAPE ({attention_type}): {mape_test:.2f}%\")\n",
    "\n",
    "    return train_losses, test_losses, y_train_actual, train_predictions_actual, y_test_actual, test_predictions_actual, mae_train, mape_train, mae_test, mape_test\n",
    "\n",
    "# Train and evaluate nonlinear attention\n",
    "train_losses_nonlinear, test_losses_nonlinear, y_train_actual, train_predictions_actual_nonlinear, y_test_actual, test_predictions_actual_nonlinear, mae_train_nonlinear, mape_train_nonlinear, mae_test_nonlinear, mape_test_nonlinear = train_model('nonlinear')\n",
    "\n",
    "# Train and evaluate linear attention\n",
    "train_losses_linear, test_losses_linear, _, train_predictions_actual_linear, _, test_predictions_actual_linear, mae_train_linear, mape_train_linear, mae_test_linear, mape_test_linear = train_model('linear')\n",
    "\n",
    "# Plot the loss curves for both models\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_losses_nonlinear, label='Training Loss (Nonlinear Attention)')\n",
    "plt.plot(test_losses_nonlinear, label='Test Loss (Nonlinear Attention)')\n",
    "plt.plot(train_losses_linear, label='Training Loss (Linear Attention)')\n",
    "plt.plot(test_losses_linear, label='Test Loss (Linear Attention)')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Test Loss Curves for Nonlinear and Linear Attention')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Plot predictions for both models\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(len(y_train_actual)), y_train_actual, label='Actual Train Prices')\n",
    "plt.plot(range(len(train_predictions_actual_nonlinear)), train_predictions_actual_nonlinear, label='Predicted Train Prices (Nonlinear)')\n",
    "plt.plot(range(len(train_predictions_actual_linear)), train_predictions_actual_linear, label='Predicted Train Prices (Linear)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Actual vs Predicted Prices for Reliance (Training Data)')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(len(y_test_actual)), y_test_actual, label='Actual Test Prices')\n",
    "plt.plot(range(len(test_predictions_actual_nonlinear)), test_predictions_actual_nonlinear, label='Predicted Test Prices (Nonlinear)')\n",
    "plt.plot(range(len(test_predictions_actual_linear)), test_predictions_actual_linear, label='Predicted Test Prices (Linear)')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Actual vs Predicted Prices for Reliance (Test Data)')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Print comparison of MAE and MAPE\n",
    "print(f\"Nonlinear Attention: Train MAE: {mae_train_nonlinear:.4f}, Train MAPE: {mape_train_nonlinear:.2f}%\")\n",
    "print(f\"Nonlinear Attention: Test MAE: {mae_test_nonlinear:.4f}, Test MAPE: {mape_test_nonlinear:.2f}%\")\n",
    "print(f\"Linear Attention: Train MAE: {mae_train_linear:.4f}, Train MAPE: {mape_train_linear:.2f}%\")\n",
    "print(f\"Linear Attention: Test MAE: {mae_test_linear:.4f}, Test MAPE: {mape_test_linear:.2f}%\")\n"
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